The Artists of Data Science

The ONLY self-development podcast for Data Scientists

The Hero's Journey | T. Scott Clendaniel on The Artists of Data Science Podcast

On this episode of The Artists of Data Science, we get a chance to hear from T. Scott Clendaniel, a leader in the data science space with over three decades of experience serving in various roles in business, analytics and artificial intelligence.

Currently, he's a chief data scientist who is aiming to create cutting edge artificial intelligence that can be made accessible to all. He gives insight into the future of A.I, how to be an effective leader, and how to use storytelling in data science.

Scott shares with us his incredible career journey and the insights he has gathered from it. This episode is packed with advice, wisdom, and tips for every data scientist to take something from. It was a great honor interviewing T. Scott!

Some notable segments from the show

[7:57] What is an A.I. winter?

[10:54] Where the field of data science is headed in the next few years?

[13:58] Tips on being an effective leader

[20:39] The underrated skill of storytelling, and how to cultivate it

[32:43] Tips for people that want to break into data science

Where to listen to the episode

Listen to the episode on Apple Podcasts, Spotify, Overcast, Stitcher, Castbox, Google Podcasts, TuneIn, YouTube, or on your favourite podcast platform.

T. Scott’s journey into data science

T. Scott’s journey began quite accidentally! He majored in strategic planning at the University of Baltimore. He heard of an opportunity to apply for a position at SteadyCorp through one of his internships. Unbeknownst to him, the position was for marketing analytics, something he was unfamiliar with. He was still able to land the job, and so began his career.

[3:40] "So I go into the interview and get all the way to the final question. And the gentleman asked me "Gee, your major seems to be strategic planning. Why would you be interested in focusing on marketing analytics?" Well, my jaw almost hit the table because the one thing that the placement officer had not told me was the job was actually for marketing analytics. I was like, well, it's so important to be able to track your return on investment and ability to do things in a market place and set up metrics. And so that's why I was interested. And somehow that worked so I started off doing analytics from that point on."

Where is the field headed in 2-5 years?

In the next two to five years, T. Scott thinks we are going to continue seeing A.I being used to solve some traditional problems. Although these areas are not “sexy”, these areas provide a big payoff. The biggest applications are actually going to be on the cost savings side and eliminating waste.

(10:54) " But more importantly, more traditional problems can be solved. And they're not nearly as sexy, but they have a lot bigger payoff. So which of my customers is going to open my e-mail? Which of my customers is going to buy? Which product? Recommender systems. From what you've seen from Amazon's been doing that forever, improving those types of areas. I think that the biggest applications are actually going to be on the cost savings side and eliminating waste and solving lots of classic classification problems, which my customers is going to buy. Which of my customers is going to default, which my customers might be a credit risk? Those type things are much lower hanging fruit, but they don't attract nearly the attention. But that's why I see the next three to five years having the biggest opportunity."

What will separate great data scientists from the rest of them?

In T. Scott’s opinion, what will separate the great data scientists from the rest is the ability to take a step back and assess what the organization really needs. Instead of creating code to solve everything, take a step back and ask yourself what the criteria for success is.

(12:35) "I think that we need to go back to say, let's look at this from the standpoint of what does the organization really need? What is the problem we're trying to solve? How are we going to define criteria for success? How are we going to say when good enough is good enough, as opposed to ultimately reaching for some unreachable state of perfection and moving more towards what happened with software development and more of an agile based approach and iterating through, I think great data scientists are going to become much more focused on how we're gonna solve this problem. What are our criteria for success? What stages can we do this in? And let's put on our problem-solving hats and stop trying to make code by itself solve everything."

Key takeaways from the episode

A.I. winter

(7:57) An A.I. winter is a period of time where the field of artificial intelligence goes fallow. This means not a whole lot of development goes on and people start to lose faith in the field. This usually happens because A.I. journalists overhype the field, causing a false narrative on future A.I capabilities.

Leadership

(13:58) To be a good leader, you have to first learn how to be a good team member. You need to be willing to focus on the greater good. You need to have a vision and the ability to get things done.

Storytelling in data science

(20:39) Storytelling is a very underrated skill that data scientists should develop. Here is a basic outline for storytelling:

Who is your hero? - It’s always the audience
What do they need to overcome?
What tool or technology are they going to use to overcome?
How is that going to happen?
What is the celebration or result of overcoming that problem going to be at the end?

What to do with these crazy jobs descriptions

(36:45) Realize that very few people have all of these crazy skills outlined in these job descriptions. If you have more than half of the requirements listed, send in the application. Also, find the job that you want to have, and check to see if other jobs have similar requirements. If you lack a certain requirement, then make sure you spend some time acquiring this skill.

Memorable quotes

(16:01) “If you're the first data scientist in an organization...make sure that you focus on a crawl, walk, run approach.”
(17:50) “Simplicity is ridiculously underrated…people do not support what they don't understand. Instead, they fear what they don't understand.”
(35:03) “Find your why and make sure it's the right why and use that to propel you…”

The one thing that T. Scott wants you to learn from his story

(46:54) Take your ego out of the equation. Be humble, and be willing to continuously learn.

From the lightning round

Best advice

Treat others the way they wish to be treated, not the way you wish to be treated.

Advice to 20 year old self

Be humble.

Topic outside of data science we should study

Graphics. Using a picture to communicate an idea does wonders to get further into your career.

Favorite interview question to ask

What concerns do you have that I might be able to address?

Recommended book

“In Search of Excellence” by Robert H. Waterman Jr. and Tom Peters.

Books and other media mentioned in this episode

“Start with Why” by Simon Sinek.

Episode transcript

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The transcript for this episode can be found here.

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